Social science surveys aimed at modeling the determinants of life course well-being often depend on the quality of retrospective reports to accurately estimate the impact of key social, economic, and health indicators. Recently developed Event History Calendar (EHC) methodologies have been shown to improve the quality of retrospective reports in comparison to traditional question-list (Q-list) approaches using both paper and pencil and computer assisted interviewing modes. However, the precise interviewing features that produce this improvement in data quality remain unspecified. This project is significant because it will address this gap in knowledge by using verbal behavior coding techniques to identify the specific interviewer-respondent interaction behaviors associated with enhanced life course retrospective recall. Specifically, this project will 1) identify the prominent retrieval and communicative patterns engendered by computer-assisted versions of the EHC and Q-list methodologies. This will be assessed through verbal behavior coding and conversational analyses of 586 audio taped interviews that were obtained in a study that compared the efficacy of EHC and Q-list interviewing techniques in terms of their ability to obtain quality retrospective reports over a lifetime response frame. 2) Using validation records from previous waves of a panel survey, this project will determine the retrieval and communicative patterns that are associated with greater completeness and accuracy in respondents' lifetime retrospective reports for each of the respective methodologies. 3) This project will determine the length of the reference periods (more recent vs. distant events) for which retrieval and communicative patterns are most effective in facilitating recall. 4) This project will contribute to the survey methodology and cognitive science literature by identifying the optimal memory cueing and conversational features to be included in health-related surveys that utilize retrospective recall measures. Ultimately, such knowledge can improve national health policy.